A Deep Learning Approach to Resolve Aliasing Artifacts in Ultrasound Color Flow Imaging.

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Abstract

Despite being used clinically as a non-invasive flow visualization tool, color flow imaging (CFI) is known to be prone to aliasing artifacts that arise due to fast blood flow beyond the detectable limit. From a visualization standpoint, these aliasing artifacts obscure proper interpretation of flow patterns in the image view. Current solutions for resolving aliasing artifacts are typically not robust against issues such as double aliasing. In this paper, we present a new dealiasing technique based on deep learning principles to resolve CFI aliasing artifacts that arise from single and double aliasing scenarios. It works by first using two convolutional neural networks (CNNs) to identify and segment CFI pixel positions with aliasing artifacts, and then it performs phase unwrapping at these aliased pixel positions. The CNN for aliasing identification is devised as a U-net architecture, and it was trained with in vivo CFI frames acquired from the femoral bifurcation that had known presence of single and double aliasing artifacts. Results show that segmentation of aliased CFI pixels was achieved successfully with intersection-over-union approaching 90%. After resolving these artifacts, the dealiased CFI frames consistently rendered the femoral bifurcation’s triphasic flow dynamics over a cardiac cycle. For dealiased CFI pixels, their rootmean- squared difference was 2.51% or less comparing to manual dealiasing. Overall, the proposed dealiasing framework can extend the maximum flow detection limit by fivefold, thereby improving CFI’s flow visualization performance.